A Hybrid Machine Learning Approach for Graduate Admission Prediction and Combined University-Program Recommendation
arXiv:2603.29881v1 Announce Type: new Abstract: Graduate admissions have become increasingly competitive. This study highlights the need for a hybrid machine learning framework for graduate admission prediction, focusing on high-quality similar applicants and a recommendation system. The dataset, collected and enriched by the authors, includes 13,000 self-reported GradCafe application records from 2021 to 2025, enriched with features from the OpenAlex API, QS World University Rankings by Subject, and Wikidata SPARQL queries. A hybrid model was developed by combining XGBoost with a residual refinement k-nearest neighbors module, achieving 87\% accuracy on the test set. A recommendation module, then built on the model for rejected applicants, provided targeted university and program alternat
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Abstract:Graduate admissions have become increasingly competitive. This study highlights the need for a hybrid machine learning framework for graduate admission prediction, focusing on high-quality similar applicants and a recommendation system. The dataset, collected and enriched by the authors, includes 13,000 self-reported GradCafe application records from 2021 to 2025, enriched with features from the OpenAlex API, QS World University Rankings by Subject, and Wikidata SPARQL queries. A hybrid model was developed by combining XGBoost with a residual refinement k-nearest neighbors module, achieving 87% accuracy on the test set. A recommendation module, then built on the model for rejected applicants, provided targeted university and program alternatives, resulting in actionable guidance and improving expected acceptance probability by 70%. The results indicate that university quality metrics strongly influence admission decisions in competitive applicant pools. The features used in the study include applicant quality metrics, university quality metrics, program-level metrics, and interaction features.
Subjects:
Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as: arXiv:2603.29881 [cs.IR]
(or arXiv:2603.29881v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2603.29881
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Elham Tabrizi [view email] [v1] Mon, 9 Feb 2026 17:25:42 UTC (1,037 KB)
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